Package: BayesS5 1.41
BayesS5: Bayesian Variable Selection Using Simplified Shotgun Stochastic Search with Screening (S5)
In p >> n settings, full posterior sampling using existing Markov chain Monte Carlo (MCMC) algorithms is highly inefficient and often not feasible from a practical perspective. To overcome this problem, we propose a scalable stochastic search algorithm that is called the Simplified Shotgun Stochastic Search (S5) and aimed at rapidly explore interesting regions of model space and finding the maximum a posteriori(MAP) model. Also, the S5 provides an approximation of posterior probability of each model (including the marginal inclusion probabilities). This algorithm is a part of an article titled "Scalable Bayesian Variable Selection Using Nonlocal Prior Densities in Ultrahigh-dimensional Settings" (2018) by Minsuk Shin, Anirban Bhattacharya, and Valen E. Johnson and "Nonlocal Functional Priors for Nonparametric Hypothesis Testing and High-dimensional Model Selection" (2020+) by Minsuk Shin and Anirban Bhattacharya.
Authors:
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BayesS5/json (API)
# Install 'BayesS5' in R: |
install.packages('BayesS5', repos = c('https://minsuk000.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 5 years agofrom:1d78d767f0. Checks:9 OK. Indexed: yes.
Target | Result | Latest binary |
---|---|---|
Doc / Vignettes | OK | Mar 15 2025 |
R-4.5-win | OK | Mar 15 2025 |
R-4.5-mac | OK | Mar 15 2025 |
R-4.5-linux | OK | Mar 15 2025 |
R-4.4-win | OK | Mar 15 2025 |
R-4.4-mac | OK | Mar 15 2025 |
R-4.4-linux | OK | Mar 15 2025 |
R-4.3-win | OK | Mar 15 2025 |
R-4.3-mac | OK | Mar 15 2025 |
Exports:Bernoulli_Uniformhyper_parind_fun_gind_fun_NLfPind_fun_pemomind_fun_pimomobj_fun_gobj_fun_pemomobj_fun_pimomresultresult_est_LSresult_est_MAPS5S5_additiveS5_parallelSSSUniform
Dependencies:abindlatticeMatrixRcppRcppArmadillosnowsnowfallsplines2